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Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease

Abstract

Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease.

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Fig. 1: Study design and tissue-level associations.
Fig. 2: Association of selected brain-related traits with cell types from the entire nervous system.
Fig. 3: Replication of associations between cell type and trait in mouse datasets.
Fig. 4: Human replication of associations between cell type and trait.
Fig. 5: Enrichment of Parkinson’s disease differentially expressed genes in cell types from the substantia nigra.

Data availability

All single-cell expression data are publicly available. Most summary statistics used in this study are publicly available. The migraine GWAS60 can be obtained by contacting the authors of that study. The full Parkinson’s disease summary statistics from 23andMe can be obtained under an agreement that protects the privacy of 23andMe research participants (https://research.23andme.com/collaborate/#publication). The 10,000 most associated SNPs from the 23andMe cohort are available in Supplementary Table 12.

Code availability

The code used to generate these results is available at https://github.com/jbryois/scRNA_disease. An R package for performing cell-type enrichments using MAGMA is also available from https://github.com/NathanSkene/MAGMA_Celltyping.

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Acknowledgements

J.B. was funded by a grant from the Swiss National Science Foundation (P400PB_180792). N.G.S. was supported by the Wellcome Trust (108726/Z/15/Z). N.G.S. and L.B. performed part of the work at the Systems Genetics of Neurodegeneration summer school funded by BMBF as part of the e:Med programme (FKZ 01ZX1704). J.H.-L. was funded by the Swedish Research Council (Vetenskapsrådet, award 2014-3863), StratNeuro, the Wellcome Trust (108726/Z/15/Z) and the Swedish Brain Foundation (Hjärnfonden). P.F.S. was supported by the Swedish Research Council (Vetenskapsrådet, award D0886501), the Horizon 2020 Program of the European Union (COSYN, RIA grant agreement no. 610307) and US NIMH (U01 MH109528 and R01 MH077139). E.A. was supported by the Swedish Research Council (VR 2016-01526), Swedish Foundation for Strategic Research (SLA SB16-0065), Karolinska Institutet (SFO Strat. Regen., Senior grant 2018), Cancerfonden (CAN 2016/572), Hjärnfonden (FO2017-0059) and Chen Zuckeberg Initiative: Neurodegeneration Challenge Network (2018-191929-5022). C.M.B. acknowledges funding from the Swedish Research Council (Vetenskapsrådet, award: 538-2013-8864) and the Klarman Family Foundation. This work is supported by the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. We thank the research participants from 23andMe and other cohorts for their contribution to this study.

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J.B., N.G.S., J.H.-L. and P.F.S. designed the study, and wrote and reviewed the manuscript; J.B. performed the analyses pertaining to Figs. 14, Extended Data Figs. 110, Supplementary Figs. 1–5 and 7–20, and Supplementary Tables 1–9 and 12–17; N.G.S performed the analyses pertaining to Fig. 5, Supplementary Fig. 6 and Supplementary Tables 10 and 11; T.F.H., L.J.A.K. and the I.H.G.C. provided the migraine GWAS summary statistics; H.J.W., the E.D.W.G.P.G.C., G.B. and C.M.B. performed the anorexia GWAS; Z.L. contributed to the revision of the manuscript, the 23andMe R.T. provided GWAS summary statistics for Parkinson’s disease in the 23andMe cohort. L.B. contributed to the post-mortem differential expression analysis (Fig. 5); E.A. provided expert knowledge on Parkinson’s disease and reviewed the manuscript.

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Correspondence to Jens Hjerling-Leffler or Patrick F. Sullivan.

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P.F.S. reports the following potentially competing financial interests: current—Lundbeck (advisory committee, grant recipient); past three years—Pfizer (scientific advisory board), Element Genomics (consultation fee) and Roche (speaker reimbursement). C.M.B. reports: Shire (grant recipient, Scientific Advisory Board member); Pearson and Walker (author, royalty recipient).

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Extended data

Extended Data Fig. 1 Enrichment of immune genes in GTEx tissues.

Enrichment pvalues of genes belonging to the GO term ‘Immune System Process’ in the 10% most specific genes in each tissue. The one-sided pvalues were computed using linear regression, testing whether the average specificity metric of the gene set was higher than 0 (z-scaled specificity metrics per tissue). The GO term was selected because it is the most associated with inflammatory bowel disease using MAGMA.

Extended Data Fig. 2 Associations of brain related traits with cell types from the entire mouse nervous system.

Associations of the top 15 most associated cell types are shown. The mean strength of association (-log10P) of MAGMA and LDSC is shown and the bar color indicates whether the cell type is significantly associated with both methods, one method or none (significance threshold: 5% false discovery rate).

Extended Data Fig. 3 Correlation in cell type associations across traits.

The Spearman rank correlations between the cell types associations across traits (-log10P) are shown. SCZ (schizophrenia), EDU (educational attainment), INT (intelligence), BMI (body mass index), BIP (bipolar disorder), NEU (neuroticism), PAR (Parkinson’s disease), MDD (Major depressive disorder), MEN (age at menarche), ICV (intracranial volume), ASD (autism spectrum disorder), STR (stroke), AN (anorexia nervosa), MIG (migraine), ALS (amyotrophic lateral sclerosis), ADHD (attention deficit hyperactivity disorder), ALZ (Alzheimer’s disease), HIP (hippocampal volume).

Extended Data Fig. 4 Associations of brain related traits with neurons from the central nervous system.

Associations of the 15 most associated neurons from the central nervous system (CNS) are shown. The specificity metrics were computed only using neurons from the CNS. The mean strength of association (-log10P) of MAGMA and LDSC is shown and the bar color indicates whether the cell type is significantly associated with both methods, one method or none (significance threshold: 5% false discovery rate).

Extended Data Fig. 5 Associations of cell types with schizophrenia/cognitive traits conditioning on gene-level genetic association of cognitive traits/schizophrenia.

MAGMA association strength for each cell type before and after conditioning on gene-level genetic association for another trait. The black bar represents the significance threshold (5% false discovery rate). SCZ (schizophrenia), INT (intelligence), EDU (educational attainment).

Extended Data Fig. 6 Replication of cell type—trait associations in 88 cell types from 9 different brain regions.

The mean strength of association (-log10P) of MAGMA and LDSC is shown for the 15 top cell types for each trait. The bar color indicates whether the cell type is significantly associated with both methods, one method or none (significance threshold: 5% false discovery rate).

Extended Data Fig. 7 Correlation in cell type associations across traits in a replication data set (88 cell types, 9 brain regions).

Spearman rank correlations for cell types associations (-log10P) across traits are shown. SCZ (schizophrenia), EDU (educational attainment), INT (intelligence), BMI (body mass index), BIP (bipolar disorder), NEU (neuroticism), PAR (Parkinson’s disease), MDD (Major depressive disorder), MEN (age at menarche), ICV (intracranial volume), ASD (autism spectrum disorder), STR (stroke), AN (anorexia nervosa), MIG (migraine), ALS (amyotrophic lateral sclerosis), ADHD (attention deficit hyperactivity disorder), ALZ (Alzheimer’s disease), HIP (hippocampal volume).

Extended Data Fig. 8 Associations of brain related traits with neurons from 9 different brain regions.

Trait—neuron association are shown for neurons of the 9 different brain regions. The specificity metrics were computed only using neurons. The mean strength of association (-log10P) of MAGMA and LDSC is shown and the bar color indicates whether the cell type is significantly associated with both methods, one method or none (significance threshold: 5% false discovery rate).

Extended Data Fig. 9 Top associated cell types with brain related traits among 24 cell types from 5 different brain regions.

The mean strength of association (-log10P) of MAGMA and LDSC is shown for the 15 top cell types for each trait. The bar color indicates whether the cell type is significantly associated with both methods, one method or none (significance threshold: 5% false discovery rate).

Extended Data Fig. 10 Top associated neurons with brain related traits among 16 neurons from 5 different brain regions.

The specificity metrics were computed only using neurons. The mean strength of association (-log10P) of MAGMA and LDSC is shown for the top 15 cell types for each trait. The bar color indicates whether the cell type is significantly associated with both methods, one method or none (significance threshold= 5% false discovery rate).

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Bryois, J., Skene, N.G., Hansen, T.F. et al. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease. Nat Genet 52, 482–493 (2020). https://doi.org/10.1038/s41588-020-0610-9

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